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• Clinical Article •
Delta MRI-based radiomics for predicting risk factors in cervical cancer patients after neoadjuvant chemotherapy
REN Zhen  LI Hongxia  MO Fan  LÜ Fajin 

DOI:10.12015/issn.1674-8034.2025.11.023.


[Abstract] Objective The primary aim of this study was to construct a predictive model utilizing Delta radiomics features derived from magnetic resonance imaging (MRI) scans taken before and after neoadjuvant chemotherapy (NACT), in order to stratify the risk of postoperative recurrence based on pathological risk factors in patients diagnosed with locally advanced cervical cancer (LACC).Materials and Methods This retrospective study enrolled 221 cervical cancer patients who underwent surgery after NACT. Based on the presence of intermediate- and high-risk pathological factors, patients were classified into a low-risk group (n = 128) and an intermediate-to-high-risk group (n = 93). Delta radiomics features were extracted and calculated from sagittal T2-weighted imaging (Sag_T2WI) and axial contrast-enhanced T1-weighted imaging (Ax_T1CE) acquired before and after NACT, followed by feature dimensionality reduction and selection. Clinical features were screened using univariate analysis. Using the random forest algorithm, we constructed separate models: a Delta-T1CE radiomics model, a Delta-T2WI radiomics model, and a clinical model. Subsequently, a dual-sequence fusion model and a clinical-dual-sequence fusion model were built. Model performance was evaluated using receiver operating characteristic (ROC) curves, and the clinical utility of the models was assessed via decision curve analysis (DCA), followed by performance evaluation and comparison.Results The areas under the curve (AUC) for the Delta-T1CE model, Delta-T2WI model, and clinical model in the test set were 0.844 [95% confidence interval (CI): 0.739 to 0.926], 0.938 (95% CI: 0.880 to 0.981), and 0.675 (95% CI: 0.543 to 0.800), respectively. Among the non-fusion models, the Delta-T2WI model demonstrated the best predictive performance. The dual-sequence fusion model and the clinical-dual-sequence fusion model showed no significant difference in performance compared to the Delta-T2WI model, with test set AUCs of 0.945 (95% CI: 0.888 to 0.986) and 0.944 (95% CI: 0.890 to 0.985), respectively. Decision curve analysis revealed that the Delta-T2WI model and the two fusion models provided higher clinical net benefit than the Delta-T1CE radiomics model and the clinical model.Conclusions Delta radiomics models, particularly those based on T2WI, can effectively predict recurrence risk stratification in cervical cancer patients after NACT, offering significant reference value for formulating treatment strategies for patients stratified into the intermediate-to-high-risk group.
[Keywords] cervical cancer；magnetic resonance imaging；radiomics；risk factors；neoadjuvant chemotherapy

REN Zhen1   LI Hongxia1   MO Fan1   LÜ Fajin1, 2*  

1 State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing 400016, China

2 Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China

Corresponding author: LÜ F J, E-mail: fajinlv@163.com

Conflicts of interest   None.

Received  2025-06-13
Accepted  2025-11-10
DOI: 10.12015/issn.1674-8034.2025.11.023
DOI:10.12015/issn.1674-8034.2025.11.023.

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